My AI learning journey

AI in performance engineering.

From parsing logs to full-stack observability: practical tools built with Python, Streamlit, and deep JVM knowledge, augmented by lightweight AI integrations for performance engineering and incident management.

// 01 · ai tools i've learned

Hands-on with the AI ecosystem.

From local inference to cloud APIs and agentic frameworks.

// 02 · the idea

Why AI belongs in performance engineering.

Closer to the telemetry

MCP servers host lightweight training and inference pipelines close to telemetry, enabling continuous fine-tuning on project data and low-latency, context-aware inference. Use them to run anomaly detectors, score and prioritize alerts, synthesize concise RCA summaries, and surface remediation suggestions, all of which reduce manual forensics and speed MTTR.

Automates incident triage Detects subtle anomalies Speeds root-cause explanation
Telemetry
MCP
train & infer
RCA
// 03 · projects

AI for incidents & observability.

Hover the pipeline steps for details. Open "More details" for the project layout and quickstart.

// 04 · themes

What ties it together.

Automated incident triageCross-source correlationAI root-cause analysisProduction dashboardsJVM heap & GC toolingStreamlit quickstartsOpen-source integrations

Goal: reduce time-to-diagnosis, make observability actionable, and ship reproducible developer tools with clear quickstarts, dashboards, and integrations.